Developing a financial analysis information base in the context of digitalization. Balance of averages

In the context of the digitalisation of the economy, the requirements users place on the results of financial analysis are changing, which necessitates the development of its methods. The article examines the shortcomings of the traditional approach to calculating average values based on balance sheet data, which are formed using only two data points — the beginning and end of the year. It is shown that the average annual values calculated in this way may not reflect the economic situation of the organisation, especially when the analysed indicator changes frequently and significantly.

The objective of the research is development of an alternative methodology, which implies the use of a weighted chronological average calculated with high time accuracy (up to seconds), facilitated by modern information accounting systems. The concept of a «balance of average values» was introduced, which is a report in the standard balance form, in which the weighted chronological average per second (WCAS) indicators are reflected. This can significantly clarify the results of analytical calculations, including indicators of turnover, profitability and business activity, and it can also expand the possibilities of a more accurate assessment of the financial condition of the reporting organisation. The use of the proposed methodology is especially relevant for external reporting users and creditors who need a reliable assessment of financial risks. The article provides an example of a calculation based on real organizational data, demonstrating a significant discrepancy between the results obtained using the traditional and proposed methods. The work is aimed at increasing the transparency and analytical value of reporting in the context of digital transformation.

The findings may be useful for specialists in the field of finance, accounting and digital analytics.

1. Kildishev G.S., Frenkel A.A. Time series analysis and forecasting. Moscow: Statistics; 1973. 104 p. (In Russ.).

2. Krichevsky M.L. Time series in management. Part 1. Moscow: RUSAINS; 2020. 219 p. (In Russ.).

3. Krichevsky M.L. Time series in management. Part 2. Moscow: RUSAINS; 2020. 230 p. (In Russ.).

4. Ginny K. Average values. Transl. from. Ital. Moscow: Statistics; 1970. 447 p. (In Russ.).

5. Woodward W.A., Gray H.L., Elliott A.C. Applied time series analysis. USA NY: CRC Press. 2011, 564 p.

6. Waller D.L. Statistics for Business. URL: https://nibmehub.com/opac-service/pdf/read/Statistics%20for%20Business%20by%20Derek%20L.%20Waller.

7. Mariappan P. Statistics for business USA: Boca Raton CRC Press; 2019. 350 p.

8. Anderson D.R., Sweeney D.J., Williams T.A. Statistics for business & economics. Boston, USA: Cengage Learning; 2018. 932 p.

9. Kovalev V.V., Kovalev Vit. In the analysis of the balance, or How to understand the balance. Moscow: Prospect; 2015. 784 p. (In Russ.).

10. Berman K., Knight J. Financial intelligence: A manager’s guide to knowing what the numbers really mean. USA, Boston: Harvard business review press; 2013. 304 p.

11. Fridson M.S., Alvarez F. Financial statement analysis. USA, New Jersey: Wiley; 2022. 429 p.

12. Wahlen J.M., Baginski S.P., Bradshaw M.T. Financial reporting, fi-nancial statement analysis, and valuation. USA, Boston.: Cengage Learning; 2017. 1058 p.

13. Subramanyam K.R. Financial statement analysis. USA, NY: McGraw-Hill Education; 2014. 795 p.

14. Penman S.H. Financial statement analysis and security valuation. USA, Columbia University: McGraw-Hill; 2013. 740 p.

15. Peterson P.P., Fabozzi F.J. Analysis financial statements. USA, NY: Wiley; 2017. 353 p.

16. Vikulina A.V. Time in milliseconds in 1C 8. URL: https://wiseadvice-it.ru/o-kompanii/blog/articles/vremyav-millisekundah-v-1s-8 (In Russ.).